I got interested in the signal that Twitter received from the two last earthquakes happening in California and Haiti. It has been recently suggested that Twitter can play a role in assessing the magnitude of an earthquake, by studying the stream of tweets that contain a reference to the event, such as the stream of messages related to #earthquake, including messages like this. The term “Twichter Scale” has been used in this context to discuss the relation between Twitter and external events such as earthquakes.

Different people have expressed different ideas about a Twichter Scale, for example:

Twichter Scale (n): the fraction of Twitter traffic caused by an earthquake. Unused on the east coast. (@ian_soboroff)

While this definition does not necessarily imply that the Twichter scale indicates the magnitude of earthquakes, it is interesting to ask whether Twitter data can be used for that purpose.

Impact of two earthquakes on different Twitter hashtag streams: #earthquake, #earthquakes and #quake between Jan 9 and Jan 15

When we look at the data, we can clearly identify both earthquakes represented as spikes in the data. Both earthquakes were comparable in terms of Magnitude (6,5 vs. 7.0 on the Richter Scale). And in fact, both events produced a comparable amplitude for the #earthquake hashtag stream. On the surface, this might be a confirmation of the idea of a Twichter Scale, based on the Richter Scale, which is a scale measuring the magnitude of an earthquake. The Richter scale produces the same value for a given earthquake, no matter where you are.

However, there is another, less scientific measure to characterize earthquakes – the so-called Mercalli scale – which is a measure of an earthquake’s effect on people and structures.

Which yields to the interesting question, whether Twitter streams can better serve as an indicator of strength (Richter) or impact (Mercalli) of an earthquake?

As we can see in the figure, the amplitude produced on Twitter is approximately equal for both events (almost 400 messages per hour). My suspicion however is, that this is not because Twitter accurately captures the strengths of earthquakes, but because the Jan 9 earthquake was closer to California, where more people (more Twitter users) are willing to share their experiences. So it seems that this produced an amplitude of similar extent, although the impact of the Jan 9 earthquake in California on structures and people was much weaker than the impact of the Jan 12 earthquake in Haiti.

So how can we identify the difference of an earthquake in terms of its impact on people and structures?

When we look at the diagram above, we can see a clear difference after the initial spike: While the Californian earthquake did not cause many follow-up tweets, the aftermath of the Haiti earthquake is clearly visible.

What does that say about Twitter as a signal for earthquakes?

The amplitude of the signal on Twitter is very likely biased by the density of Twitter users in a given region, and thereby can neither give reliable information about the magnitude nor the impact of an earthquake. This suggests that Twitter can not act as a reliable sensor to detect the magnitude of an earthquake in a “Richter Scale” sense.

However, the “aftermath” of a spike on twitter (the integral) seems to be a good indication of an earthquake’s impact on people and structures – in a “Mercalli Scale” sense. Long after the initial spike, the Haiti earthquake is still topic of conversations on Twitter (those are likely related to fundraising efforts and other related aid activities). Indepentent of the density of Twitter users in Haiti (which is probably low), the aftermath can clearly be identified.

The Twicalli Scale:

This suggests that Twitter as a sensor for the magnitude of earthquakes (in a Richter Scale sense) does not seem very useful. Twitter is more indicative of earthquakes in a “Twicalli scale” sense:

Using the aftermath (not the amplitude) of twitter stream data, the impact (not the magnitude) of earthquakes becomes visible on Twitter.

Update: Here are links to further resources and the datasets this analysis is based on:

Update II (Aug 27 2010): The Twicalli scale was mentioned in a recent paper on the importance of trust in social awareness streams such as Twitter (page 8, left column)

Marcelo Mendoza, Barbara Poblete and Carlos Castillo, Twitter Under Crisis: Can we trust what we RT?, Workshop on Social Media Analytics, In conjunction with the International Conference on Knowledge Discovery & Data Mining (KDD 2010), PDF download (see page 8, left column)

Update III (Oct 11 2011): Now there’s also a WWW2011 paper mentioning the Twicalli Scale (page 2, top of right column)

About me

Markus Strohmaier, Full Professor of Web-Science at the Faculty of Computer Science at University of Koblenz-Landau (Germany) and Scientific Director at GESIS - the Leibniz Institute for the Social Sciences (Germany).

My research focuses on the World Wide Web, my interests include social computation, agents, online production systems and crowdsourcing.